How NLP changed the abilities of chatbots
A chatbot is a great way to make internet communication more pleasant for both customers and companies. At the beginning of the millennium, people would probably laugh at you for this sentence. But not today. The main reason for this change is Natural Language Processing (NLP). It is this branch of artificial intelligence science, that has transformed the clumsy and cumbersome automata into today’s clever chatbots, which you can hardly tell from people sometimes.
Thanks to NLP, artificial intelligence learns to understand something as complex as human communication. The natural speech that people use to speak to each other is entirely different from the abbreviations, phrases, and slogans spoken by the first chatbots. Thanks to NLP, AI is finally beginning to understand its creators.
The abbreviation NLP covers knowledge from the development of artificial intelligence, linguistics, mathematics, and machine learning. All of this is needed for AI to accept, whether in the form of sound or text, a natural human message, to understand it, and to be able to respond to it correctly. Thanks to NLP, human language moves from entering slogans and weird sentences to a full-fledged conversation when communicating with chatbots.
Before there was NLP
It is the knowledge from NLP that allows chatbots to do more than what programmers directly put into them. A chatbot without NLP cannot leave the programmed tracks, and any received speech that deviates from them will not be processed. The solution simply doesn’t understand it — and the writer has no choice but to communicate in the slogans that the chatbot requires. Thus, any naturalness or pleasantness of communication disappears. And with it, for example, the desire to shop where this chatbot will stand in your way.
NLP frees chatbots (and voicebots) from these tracks and unbinds them from the shackles of preset patterns and slogans. Chatbots with NLP not only perceive the meaning of words but also understand whole sentences or even the context and intentions of the person on the other side of the conversation.
NLP doesn’t just make life easier for those with whom you can chat. It also helps programmers. If you want to learn a chatbot without NLP, the answer to the question: How much will I pay for this tariff per month? You have to teach him word by word every variant of the question or rely on preset buttons. With NLP, however, the customer doesn’t need to hit the exact combination of words that the chatbot knows. The solution understands everything from the context and eventually asks for the missing information himself.
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The chatbot’s ability to learn and improve independently is one of the biggest merits of NLP. From slavish input of words and phrases, the work of developers has changed to the training of true artificial intelligence. And over time, the chatbots began to learn on their own. Not only during their creation but also during operation. Each new conversation brings them new data to better understand the expressions and contexts common in interpersonal communication. A chatbot is gradually improving as if a human operator were in its place.
In addition, a virtual assistant equipped with NLP will not be taken aback by the flaws that are common in written human communication. Thanks to its knowledge and ability to work with context, it can handle typos and grammatical errors, while one customer’s typo eliminated its predecessors from the game.
But even a chatbot equipped with NLP capabilities cannot converse about everything from the latest fashion trends to nuclear physics. But it can chat fluently in the area of specialization of the company that had the solution developed. So if you meet it, for example, on the website of a mobile operator company, it can solve with you the same things that its human colleagues could do in the same place. But don’t ask it, as well as its human colleagues, for advice on what to wear this summer.
Between questions and answers
But what does the NLP chatbot, which just got your message, thought process look like? The path to the response consists of five steps that ideally happen in a flash sequence — tokenizing, normalization, entity recognition, dependency parsing, and generation.
Step 1 — Tokenizing: Chatbot splits the message into small pieces of information
Step 2 — Normalizing: Chatbot corrects errors, typos, and slang expressions
Step 3 — Recognizing Entities: Chatbot determines what the words refer to. Watermelon is determined as the fruit, Mount Everest as the mountain, and 55 as the number.
Step 4 — Dependency Parsing: Chatbot divides words into nouns, verbs, phrases, and other grammatical units
Step 5 — Generation: Chatbot generates possible answers and selects the most suitable one, which it then sends
Despite the development of NLP, chatbots do not have it easy with human communication. And so, even the best developed and long-trained assistants are not infallible. Synonymous expressions, typos that give new meaning to words, abbreviations, or overly colloquial expressions can be an insurmountable obstacle. Even so, NLP opened the door to chat fields for relevant and desirable virtual helpers.
And it’s no wonder. A well-developed chatbot does a lot of work in almost any company. For example, work that increases sales improves the customer experience or relieves congested human operators.
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